Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework
Abstract
There is a great range of spectacular coral reefs in the ocean world. Unfortunately, they are in jeopardy, due to an overabundance of one specific starfish called the coral-eating crown-of-thorns starfish (or COTS). This article provides research to deliver innovation in COTS control. Using a deep learning model based on the You Only Look Once version 5 (YOLOv5) deep learning algorithm on an embedded device for COTS detection. It aids professionals in optimizing their time, resources and enhancing efficiency for the preservation of coral reefs all around the world. As a result, the performance over the algorithm was outstanding with Precision: 0.93 - Recall: 0.77 - F1-score: 0.84.
Supporting Agencies
Keywords:
deep learning; computer vision; YOLO; embedded systemReferences
L. Jiajun, K. Brano, M. Ross, D. Brendan, M. Torsten, C. Joey, S. Andy, H. Nic, V. R. Karl, T. S. Lachlan, A. A. David, A. A. Mohammad, C. Geoffrey, B. Russ, M. Peyman, S. Daniel, D. Tim, E.M. Kemal, W. Martin, M. Megha, The CSIRO Crown-of-Thorn Starfish Detection Dataset, arXiv 2021, https://doi.org/10.48550/arXiv.2111.14311
Google Scholar
W. Junlong, K. Wei, Z. Wei, H. Fengbiao, T. Xuefeng, W. Qiong, Helmet Detection Algorithm Based on the Improved YOLOv5 and Dynamic Anchor Box Matching, Proceedings of the IEEE International Conference on Emergency Science and Information Technology (ICESIT) (2021) 79-83, http://dx.doi.org/10.1109/ICESIT53460.2021.9696525.
DOI: https://doi.org/10.1109/ICESIT53460.2021.9696525
Google Scholar
Y. Zhong, J. Wang, J. Peng, L. Zhang, Anchor Box Optimization for Object Detection, Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) (2020) 1275-1283, http://dx.doi.org/10.1109/WACV45572.2020.9093498.
DOI: https://doi.org/10.1109/WACV45572.2020.9093498
Google Scholar
T. F. Dima, M. E. Ahmed, Using YOLOv5 Algorithm to Detect and Recognize American Sign Language, Proceedings of the International Conference on Information Technology (ICIT) (2021) 603-607, http://dx.doi.org/10.1109/ICIT52682.2021.9491672.
DOI: https://doi.org/10.1109/ICIT52682.2021.9491672
Google Scholar
G. Verma, Y. Gupta, A. M. Malik, B. Chapman, Performance Evaluation of Deep Learning Compilers for Edge Inference, Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (2021) 858-865, http://dx.doi.org/10.1109/IPDPSW52791.2021.00128.
DOI: https://doi.org/10.1109/IPDPSW52791.2021.00128
Google Scholar
T. Zhi, S. Chunhua, C. Hao, H. Tong, FCOS: Fully Convolutional One-Stage Object Detection, arXiv 2019, https://doi.org/10.48550/arXiv.1904.01355.
Google Scholar
L. Wei, A. Dragomir, E. Dumitru, S. Christian, R. Scott, F. Cheng-Yang, B. C. Alexander, SSD: Single Shot MultiBox Detector, Lecture Notes in Computer Science, 2016, https://doi.org/10.1007/978-3-319-46448-0_2.
DOI: https://doi.org/10.1007/978-3-319-46448-0_2
Google Scholar
Z. Ni, J. Chen, N. Sang, C. Gao, L. Liu, Light YOLO for high-speed gesture recognition, Proceedings of The 2018 25th IEEE International Conference on Image Processing (ICIP) (2018) 3099-3103, http://dx.doi.org/10.1109/ICIP.2018.8451766.
DOI: https://doi.org/10.1109/ICIP.2018.8451766
Google Scholar
A. Aleena, S. Ayesha, J. Tauseef, U.K. Asif, Small Object Detection using Deep Learning, arXiv 2022, https://doi.org/10.48550/arXiv.2201.03243.
Google Scholar
B. G. Han, J. G. Lee, K. T. Lim, D. H. Choi, Design of a scalable and fast YOLO for edge-computing devices, Sensors 20(23) (2020) 6779-6794, https://doi.org/10.3390/s20236779.
DOI: https://doi.org/10.3390/s20236779
Google Scholar
B. Liang, S. Wu, K. Xu, J. Hao, Butterfly detection and classification based on integrated YOLO algorithm, arXiv 2020, https://doi.org/10.48550/arXiv.2001.00361.
DOI: https://doi.org/10.1007/978-981-15-3308-2_55
Google Scholar
C. Shaobin, L. Wei, Embedded System Real-Time Vehicle Detection based on Improved YOLO Network, Proceedings of the IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (2019) 1400-1403, http://dx.doi.org/10.1109/IMCEC46724.2019.8984055.
DOI: https://doi.org/10.1109/IMCEC46724.2019.8984055
Google Scholar
Y. Zhu, C. Yao, X. Bai, Scene text detection and recognition: recent advances and future trends, Frontiers of Computer Science 10 (2016) 19-36, http://dx.doi.org/10.1007/s11704-015-4488-0.
DOI: https://doi.org/10.1007/s11704-015-4488-0
Google Scholar
Q. Lu, Y. Yuan, Improved YOLO Algorithm for Object Detection in Traffic Video, Proceedings of the International Conference in Communications, Signal Processing and Systems (2019) 1647-1655, http://dx.doi.org/10.1007/978-981-13-9409-6_198.
DOI: https://doi.org/10.1007/978-981-13-9409-6_198
Google Scholar
R. Shaoqing, H. Kaiming, G. Ross, S. Jian, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv 2016, https://doi.org/10.48550/arXiv.1506.01497.
Google Scholar
H. Shijie, W. Zhonghao, S. Fuming, LEDet: A Single-Shot Real-Time Object Detector Based on Low-Light Image Enhancement, The Computer Journal 64(7) 2021 1028-1038, https://doi.org/10.1093/comjnl/bxab055.
DOI: https://doi.org/10.1093/comjnl/bxab055
Google Scholar
A. A. Choudhury, R. Saha, S. Z. Shoumo, S. R. Tulon, J. Uddin, M. K. Rahman, An efficient way to represent braille using YOLO algorithm, Proceedings of the Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) (2018) 10-19, http://dx.doi.org/10.1109/ICIEV.2018.8641038.
DOI: https://doi.org/10.1109/ICIEV.2018.8641038
Google Scholar
D. Volivier, M. Saïd, A. M. Sidi, M. Pierre, L. Frédéric, A new Edge Architecture for AI-IoT services deployment, Proceedings of the 17th International Conference on Mobile Systems and Pervasive Computing (MobiSPC), (2020) 10-19, https://doi.org/10.1016/j.procs.2020.07.006.
DOI: https://doi.org/10.1016/j.procs.2020.07.006
Google Scholar
A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, Communications of the ACM 60(6) (2017) 84-90, https://doi.org/10.1145/3065386.
DOI: https://doi.org/10.1145/3065386
Google Scholar
D. S. Viraktamath, P. Navalgi, A. Neelopant, Comparison of YOLOv3 and SSD Algorithms, Proceedings of the International Journal of Engineering Research & Technology (IJERT) (2021) 1156-1160.
Google Scholar
J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 779-788, http://dx.doi.org/10.1109/CVPR.2016.91.
DOI: https://doi.org/10.1109/CVPR.2016.91
Google Scholar
A. Bochkovskiy, C. Y. Wang, H. Y. M. Liao, YOLOv4: Optimal speed and accuracy of object detection, arXiv 2020, https://doi.org/10.48550/arXiv.2004.10934.
Google Scholar
B. Jiang, R. Luo, J. Mao, T. Xiao, Y. Jiang, Acquisition of Localization Confidence for Accurate Object Detection, In Proceedings of the European conference on computer vision (ECCV) (2018) 8-14, http://dx.doi.org/10.1007/978-3-030-01264-9_48.
DOI: https://doi.org/10.1007/978-3-030-01264-9_48
Google Scholar
Z. Zheng, P. Wang, D. Ren, W. Liu, R. Ye, Q. Hu, W. Zuo, Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation, arXiv 2021, https://doi.org/10.48550/arXiv.2005.03572.
DOI: https://doi.org/10.1109/TCYB.2021.3095305
Google Scholar
K. K. Reddy, M. Shah, Recognizing 50 human action categories of web videos, Machine Vision and Applications 24 (2013) 971-981, https://doi.org/10.1007/s00138-012-0450-4
DOI: https://doi.org/10.1007/s00138-012-0450-4
Google Scholar
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